Lipid profiles as potential mediators linking body mass index to osteoporosis among Chinese adults: the Henan Rural Cohort Study

The impact of body mass index on the relationship between psoriasis and Osteopenia: a mediating analysis based on NHANES (2003-2006)

The relationship between psoriasis and osteopenia remains undetermined. Patients with psoriasis tend to have a higher Body Mass Index (BMI) compared to those without the condition. While it appears plausible that BMI could mediate this association, further study is required to confirm this hypothesis. The objective of this study is to ascertain whether BMI plays a role in influencing the impact of psoriasis on osteopenia. This study encompassed 2,624 participants from the National Health and Nutrition Examination Survey (NHANES) conducted between 2003 and 2006. The condition of psoriasis was self-reported, while osteopenia was assessed based on bone mineral density (BMD) range and self-reported osteoporosis. BMI was derived from NHANES body measurement data. Weighted logistic regression analyses and mediation analysis were utilized to elucidate the relationship. Subgroup differences were further explored in the absence of a clear relationship. A positive correlation was observed between psoriasis and osteopenia. Furthermore, BMI was positively related to psoriasis and negatively related to osteopenia. Additionally, BMI served as a mediator in the relationship between psoriasis and osteopenia, accounting for 20.8% of the variance. Specifically, the mediating influence of BMI exhibited variations based on diabetes status and gender. In conclusion, Controlling BMI could potentially mitigate the impact of psoriasis on osteopenia. Therefore, we advocate for a rigorous focus on bone health in individuals with psoriasis, particularly among males and non-diabetic populations.

Sarcopenia and osteoporosis in patients with psoriatic arthritis: A single-center retrospective study

OBJECTIVE

The risk of both osteoporosis and sarcopenia is high in inflammatory diseases, but there have been few reports of psoriatic arthritis (PsA). This study aimed to evaluate the rate of sarcopenia and osteoporosis, and the association of sarcopenia with osteoporosis in patients with PsA at our institution.

METHODS

The data in this study were extracted from 320 patients with PsA meeting CASPAR criteria diagnosed between January 2010 and December 2021. The 156 patients who had undergone body composition measurements with dual-energy X-ray absorptiometry were included.

RESULTS

Overall, the rate of sarcopenia and presarcopenia were 5.1% and 16.7%. Body mass index (BMI) was significantly lower in the presarcopenia and sarcopenia group. Furthermore, the presarcopenia and sarcopenia group had a significantly lower T-score in all regions. Multivariate analysis of the determinants of T-score for each site showed that SMI was significantly involved for the lumbar spine and the femoral neck, and BMI and rheumatoid factor positivity for the total hip.

CONCLUSIONS

In patients with PsA, the rate of sarcopenia was 5.1%. Osteoporosis rates for males and females were 5.7% and 7.5%, respectively. SMI, T-score, and BMI are significantly correlated with each other and should be considered in clinical practice.

Osteoporosis and osteopenia in patients with psoriatic arthritis: A single-centre retrospective study

OBJECTIVE

It is known that fracture risk is increased in patients with psoriatic arthritis (PsA); however, there is no consensus on the association with osteoporosis. The purpose of this study was to elicit the rate of osteoporosis and the risk factors of osteoporosis in patients with PsA at our institution.

METHODS

The data in this study were extracted from 163 patients with PsA. Osteoporosis and osteopenia were defined based on the WHO definition. Osteoporosis was also diagnosed when a fragility vertebral compression fracture was observed.

RESULTS

The osteoporosis and osteopenia rates for PsA patients were 11.7% and 33.1%, respectively. The rates of osteoporosis and osteopenia in males were particularly high compared to previous reports, at 9.3% and 34.3%, respectively. Trabecular bone score was considered age-appropriate for both males and females. Body mass index and Trabecular bone score were significantly lower in patients with osteoporosis.

CONCLUSIONS

In patients with PsA, males are at elevated risk of osteoporosis and associated fragility fractures even if they are under 50 years. Body mass index was significantly lower in osteoporotic cases, suggesting the importance of bone mineral density testing and treatment in such cases.

Unraveling the role of BMI and blood markers in the relationship between plant-based diets and osteoporosis: A prospective cohort study

BACKGROUND

The potential adverse effects of plant-based diets on bone health have raised significant concern, while the prospective evidence is limited. This study aimed to evaluate the association between plant-based diet indexes and incident osteoporosis while exploring the underlying mechanisms involved in this relationship.

METHODS

The analysis included 202,063 UK Biobank participants conducted between 2006 and 2022. Plant-based diet indexes (hPDI and uPDI) were calculated using the 24-h dietary questionnaire. Cox proportional risk regression and mediation analysis were used to explore the associations of plant-based diet indexes with osteoporosis, estimating the contribution of BMI and blood markers.

RESULTS

We found the highest quintile for hPDI (HR = 1.16; 95% CI: 1.05 to 1.28) and uPDI (HR = 1.15; 95% CI: 1.05 to 1.26) were associated with an increased risk of osteoporosis. BMI was identified as an important mediator in the association between hPDI and osteoporosis, with mediation proportions of 46.17%. For blood markers, the mediating (suppressing) effects of C-reactive protein, alkaline phosphatase, and insulin-like growth factor-1 on the association between uPDI (hPDI) and osteoporosis were significant, ranging from 5.63%-16.87% (4.57%-6.22%).

CONCLUSION

Adherence to a plant-based diet is associated with a higher risk of osteoporosis, with BMI and blood markers potentially contributing to this relationship. Notably, even a healthy plant-based diet necessitates attention to weight management to mitigate its impact on bone loss. These findings emphasize the importance of personalized dietary recommendations and lifestyle interventions to decrease the risk of osteoporosis.

The effect of overweight or obesity on osteoporosis: A systematic review and meta-analysis

BACKGROUND

Osteoporosis and obesity are closely related, and the relationships between different types of obesity and osteoporosis are inconsistent.

OBJECTIVE

Our objective was to summarize earlier data concerning the association between osteoporosis and obesity (general and central), and to compare the impacts of these two obesity indicators on osteoporosis.

METHODS

From inception to May 2021, a comprehensive search in electronic bibliographic databases was conducted, and the search was updated in December 2021, July 2022 and June 2023. The data were independently extracted and evaluated by two investigators from epidemiological studies that reported the impact of obesity on the odds of incident osteoporosis.

RESULTS

There were 24 studies included in the final analysis when it came to general obesity measured by body mass index (BMI). Individuals with overweight and obesity had decreased odds of osteoporosis (odds ratio (OR), 0.451, 95% confidence intervals (CIs): 0.366-0.557). Sensitivity analyses showed that both overweight and obesity were decreased odds of osteoporosis, with reductions of 48.6% and 70.1%, respectively (OR, 0.514, 95% CI: 0.407-0.649; OR, 0.299, 95% CI: 0.207-0.433). Conversely, individuals classified as underweight were found to have higher odds of osteoporosis (OR, 2.540, 95% CI: 1.483-4.350). In term of central obesity, the final analysis consisted of 7 studies. No significant association was observed between central obesity and osteoporosis (OR, 0.913, 95% CI: 0.761-1.096).

CONCLUSIONS

General overweight and obesity were associated with lower odds of developing osteoporosis, whereas underweight was associated with higher odds. However, central obesity did not show a significant association with osteoporosis. These findings underscore the importance of considering the impact of obesity on osteoporosis. Further research is necessary to reinforce the evidence and validate our findings.

Relationship Between Weight-Adjusted Waist Index and Osteoporosis in the Senile in the United States from the National Health and Nutrition Examination Survey, 2017-2020

BACKGROUND

Some studies suggested obesity may be beneficial in preventing bone loss through the negative relationship between body mass index (BMI) and osteoporosis in senile. However, using BMI to measure obesity is unconvincing due to confounding factors such as muscle mass were not taken into account, and few articles have yet taken a better way to evaluate the relationship between obesity and osteoporosis.

METHODOLOGY

Using a cross-sectional sample of 1,979 participants aged ≥65 years from the National Health and Nutrition Examination Survey (NHANES) 2017 to 2020, we evaluated the relation of weight-adjusted waist index (WWI) with osteoporosis. WWI was calculated as waist (cm) divided by the square root of body weight (kg). Diagnosis of osteoporosis was described as follows: according to the updated reference for calculating bone mineral density T-Scores, we marked the BMD value as X, using the formula T _{femoral neck}= (X g/cm²-0.888 g/cm²)/0.121 g/cm², T _{lumbar spine}= (X g/cm²- 1.065 g/cm²)/0.122 g/cm², and defined those with a final T _{femoral neck} <-0.25. T _{lumbar spine}<-0.25 or patients with previously diagnosed OP in other hospitals as osteoporosis.

RESULTS

All the 1,979 participants were between 65 and 80 years, there were 379 (21.1%) with osteoporosis, 608 (30.7%) with WWI exceeding 12 (cm/√kg) (range 8.85-14.14), and 955 (48.3%) women. Furthermore, the relationship between WWI and osteoporosis was nonlinear with a threshold effect point. Odds of OP significantly increased with the increase of WWI (OR 2.33, 95% CI 11.48-3.38, P = 0.0001) at the right side of the threshold point (WWI≥12) according to the threshold effect study.

CONCLUSIONS

Found a significant positive relationship between WWI and osteoporosis. Body fat management in the senile may be good to prevent osteoporosis if confirmed by other prospective studies analyzing the longitudinal risk of osteoporosis with obesity.

Blood Profiles in the Prediction of Radioiodine Refractory Papillary Thyroid Cancer: A Case-Control Study

Purpose

Although most patients with papillary thyroid cancer can be cured by surgery and I-131 ablation, a small proportion will progress to radioactive iodine refractory (RAIR) thyroid cancer. The prediction of RAIR in its early stages can improve patient prognosis. The aim of this article is to evaluate the blood biomarkers in patients with RAIR and to establish a prediction model.

Patients and Methods

Data collected from patients with thyroid cancer that were enrolled from Jan. 2017 to Dec. 2021 were screened. RAIR was defined based on the criteria in the 2015 American Thyroid Association guidelines. The blood biomarkers from the study participants at three admissions timepoints (surgery and first and secondary I-131 ablations) were compared using both parametric and nonparametric tests to identify predictive factors for RAIR. Binary logistic regression analysis was used to construct a prediction model using parameters associated with surgical procedure decision. The model was then assessed with receiver operating characteristic curves.

Results

Thirty-six patients were included in the data analysis. Sixteen blood variables, including the low density lipoprotein-cholesterol-total cholesterol ratio, neutrophils, thyroglobulins, thyroglobulin antibody, thyroid peroxidase antibody, anion gap, etc., were revealed to be predictors for RAIR. The prediction model, which incorporated two parameters, reached an area under the curve of 0.861 (p<0.001).

Conclusion

Conventional blood biomarkers can be used in the prediction of early-stage RAIR. In addition, a prediction model incorporating multiple biomarkers can improve the predictive accuracy.

Associations of the Geriatric Nutritional Risk Index With Femur Bone Mineral Density and Osteoporosis in American Postmenopausal Women: Data From the National Health and Nutrition Examination Survey

Background

The geriatric nutritional risk index (GNRI) has been used as a significant tool to access the nutritional status of the elderly. However, the relationship between the GNRI and femur bone mineral density (BMD) and the risk of osteoporosis remains unclear in American postmenopausal women.

Objectives

We aimed to explore associations between the GNRI with femur BMD and the risk of osteoporosis in American postmenopausal women.

Methods

We merged the continuous National Health and Nutrition Examination Survey (NHANES) 2005–2006, 2007–2008, 2009–2010, 2013–2014, and 2017–2018 to ensure a large and representative sample, including 3,152 participants. The linear relationship between the GNRI and femur BMD was assessed via a weighted multivariate linear regression model. The odds ratios (ORs) and 95% confidence intervals (95% CIs) for the association between the GNRI and the risk of osteoporosis were assessed by a weighted logistic regression model. Moreover, the nonlinear relationship was also characterized by smooth curve fitting (SCF) and a weighted generalized additive model (GAM).

Results

After adjusting for all covariates, the weighted multivariable linear regression models demonstrated that the GNRI was positively correlated with femur BMD. The weighted logistic regression models demonstrated that each unit of increased GNRI value was associated with a decreased risk of osteoporosis of 4.13%. When categorizing GNRI based on quartiles, ORs between the risk of osteoporosis and the GNRI across quintiles 2, 3, and 4 compared with quintile 1 were 0.5565 (95% CI: 0.4791, 0.6463; P &lt; 0.000001), 0.5580 (95% CI: 0.4600, 0.6769; P &lt; 0.000001), and 0.3475 (95% CI: 0.2681, 0.4505; P &lt; 0.000001). The trends similar to the above were also observed in SCF and GAM.

Conclusion

This study indicated that nutritional status, represented by the GNRI, was positively associated with femur BMD and negatively associated with the risk of osteoporosis in American postmenopausal women. The GNRI may be a good tool to identify American postmenopausal women who need further bone health nutritional support.

Novel nomogram for predicting the 3-year incidence risk of osteoporosis in a Chinese male population

OBJECTIVE

To establish a rapid, cost-effective, accurate, and acceptable osteoporosis (OP) screening model for the Chinese male population (age ≥ 40 years) based on data mining technology.

MATERIALS AND METHODS

This was a 3-year retrospective cohort study, which belonged to the sub-cohort of the Chinese Reaction Study. The research period was from March 2011 to December 2014. A total of 1834 subjects who did not have OP at the baseline and completed a 3-year follow-up were included in this study. All subjects underwent quantitative ultrasound examinations for calcaneus at the baseline and follow-ups that lasted for 3 years. We utilized the least absolute shrinkage and selection operator (LASSO) regression model to select feature variables. The characteristic variables selected in the LASSO regression were analyzed by multivariable logistic regression (MLR) to construct the predictive model. This predictive model was displayed through a nomogram. We used the receiver operating characteristic (ROC) curve, C-index, calibration curve, and clinical decision curve analysis (DCA) to evaluate model performance and the bootstrapping validation to internally validate the model.

RESULTS

The predictive factors included in the prediction model were age, neck circumference, waist-to-height ratio, BMI, triglyceride, impaired fasting glucose, dyslipidemia, osteopenia, smoking history, and strenuous exercise. The area under the ROC (AUC) curve of the risk nomogram was 0.882 (95% CI, 0.858-0.907), exhibiting good predictive ability and performance. The C-index for the risk nomogram was 0.882 in the prediction model, which presented good refinement. In addition, the nomogram calibration curve indicated that the prediction model was consistent. The DCA showed that when the threshold probability was between 1 and 100%, the nomogram had a good clinical application value. More importantly, the internally verified C-index of the nomogram was still very high, at 0.870.

CONCLUSIONS

This novel nomogram can effectively predict the 3-year incidence risk of OP in the male population. It also helps clinicians to identify groups at high risk of OP early and formulate personalized intervention measures.

High Body Mass Index and Triglycerides Help Protect against Osteoporosis in Patients with Type 2 Diabetes Mellitus

PURPOSE

This study was conducted to investigate whether high body mass index (BMI) and triglycerides (TGs) were protective factors for reducing osteoporosis (OP) in patients with type 2 diabetes mellitus (T2DM). Participants and Methods. Seventy-nine patients (aged 20 to 81) with T2DM were included in the study. Basic information and blood indicators were collected. Bone mineral density was used to diagnose OP. Participants were grouped according to BMI (normal weight vs. overweight/obese participants), TG (normal TG vs. hypertriglyceridemia), and OP (non-OP vs. OP), and differences were compared between groups. Regression analysis was used to explore whether BMI or TG were independent factors affecting OP.

RESULTS

The proportions of OP in the overweight/obese and hypertriglyceridemic groups were significantly lower than those in the normal weight (30.0% vs. 69.0%; P = 0.001) and normal TG (27.3% vs. 56.5%; P = 0.010) groups. In the OP group, the BMI (24.8 ± 3.4 vs. 26.6 ± 2.5 kg/m2; P = 0.009) was significantly lower than that in the non-OP group, and TG showed the same trend (1.30 (0.81) vs. 1.71 (1.1) mmol/L; P = 0.020). Logistic regression in the crude model showed that the odds ratios (ORs) of OP in the overweight/obese and hypertriglyceridemic groups were 0.193 (95% CI: 0.071, 0.520) and 0.315 (95% CI: 0.119, 0.830) compared with those of the normal weight and normal TG groups. After adjusting for sex and smoking, the ORs were 0.204 (95% CI: 0.074, 0.567) and 0.242 (95% CI: 0.082, 0.709) for the overweight/obese and hypertriglyceridemic groups, respectively. After adjusting for all confounding factors, the ORs for these groups were 0.248 (95% CI: 0.083, 0.746) and 0.299 (95% CI: 0.091, 0.989), respectively.

CONCLUSION

BMI and TG are independent protective factors against OP in patients with T2DM.

Long-term exposure to air pollution might increase prevalence of osteoporosis in Chinese rural population

OBJECTIVES

The associations of long-term exposure to air pollution with osteoporosis are rarely reported, especially in rural China. This study aimed to explore the association among rural Chinese population.

METHODS

A total of 8033 participants (18-79 years) derived from the Henan Rural Cohort Study (n = 39,259) were included in this cross-sectional study. Exposure to air pollutants was estimated using machine learning algorithms with satellite remote sensing, land use information, and meteorological data [including particulate matter with aerodynamic diameters ≤1.0 μm (PM1), ≤2.5 μm (PM2.5), and ≤10 μm (PM10), and nitrogen dioxide (NO2)]. The bone mineral density of each individual was measured by using ultrasonic bone density apparatus and osteoporosis was defined based on the T-score ≤ -2.5. Multiple logistic regression models were used to examine the association of air pollution and osteoporosis prevalence.

RESULTS

We observed that per 1 μg/m3 increase in PM1, PM2.5, PM10 and NO2 were associated with a 14.9%, 14.6%, 7.3%, and 16.5% elevated risk of osteoporosis. Compared with individuals in the first quartile, individuals in the fourth quartile had higher odds ratio (OR) of osteoporosis (P-trend < 0.001), the ORs (95% confidence interval) were 2.08 (1.72, 2.50) for PM1, 2.28 (1.90, 2.74) for PM2.5, 1.93 (1.60, 2.32) for PM10, and 2.02 (1.68, 2.41) for NO2. It was estimated that 20.29%-24.36% of osteoporosis cases could be attributable to air pollution in the rural population from China.

CONCLUSIONS

Long-term exposure to air pollutants were positively associated with high-risk of osteoporosis, indicated that improving air quality may be beneficial to improve rural residents health.

Adiposity reduces the risk of osteoporosis in Chinese rural population: the Henan rural cohort study

Background

Adiposity plays a crucial role in the risk of osteoporosis. However, the impact of body fat distribution on the skeleton is contentious. The study was designed to explore the association of various adiposity indices with estimated bone mineral density (BMD) and the risk of osteoporosis based on body mass index (BMI), body fat percentage (BFP), waist circumference (WC), waist to hip ratio (WHR), waist to height ratio (WHtR), and visceral fat index (VFI).

Methods

A total of 8475 subjects derived from the Henan Rural Cohort Study were analyzed. The estimated BMD of study participants were measured by calcaneal quantitative ultrasound (QUS). Linear regression and binary logistic regression were performed to estimate the association of adiposity and the outcomes.

Results

The mean age of the study participants was 55.23 ± 11.09 years and 59.61% were women. The crude and age-standardized prevalence of high osteoporosis risk was 16.24 and 11.82%. Per unit increment in adiposity indices was associated with 0.005–0.021 g/cm² increase in estimated BMD. The adjusted odds ratios (95% confidence interval) for high osteoporosis risk in per 1 SD increase of WC, WHR, WHtR, BMI, BFP, and VFI were 0.820 (0.748, 0.898), 0.872 (0.811, 0.938), 0.825 (0.765, 0.891), 0.798 (0.726, 0.878), 0.882 (0.800, 0.972), and 0.807 (0.732, 0.889), respectively. Stratified analyses indicated greater effects on individuals aged 55 years or older.

Conclusions

The adiposity indices have an inverse association with the risk of osteoporosis among Chinese rural population, especially in the elderly.

Gender-specific prevalence and influencing factors of osteopenia and osteoporosis in Chinese rural population: the Henan Rural Cohort Study

OBJECTIVE

The aims of this study were to describe distributions of the prevalence of osteopenia and osteoporosis and identify the potential risk factors by gender in a Chinese rural population.

DESIGN

A cross-sectional survey.

SETTING AND PARTICIPANTS

A total of 8475 participants (18-79 years) were obtained from the Henan Rural Cohort Study. Bone mineral density (BMD) of the calcaneus for each individual was measured by ultrasonic bone density apparatus. Logistic regression models were used to evaluate associations of potential risk factors with prevalence of osteopenia and osteoporosis. Furthermore, a meta-analysis of prevalence of osteoporosis which included eight studies was conducted to confirm this study results.

RESULTS

The mean of BMD were 0.42 and 0.32 g/cm² for men with osteopenia and osteoporosis (p<0.001), as well as 0.40 and 0.30 g/cm² (p<0.001) for women with osteopenia and osteoporosis, respectively. The overall age-standardised prevalence of osteopenia and osteoporosis were 42.09% and 11.76% in all participants. The age-standardised prevalence of osteopenia in men (45.98%) was significantly higher than that in women (39.73%), whereas the age-standardised prevalence of osteoporosis in men (7.82%) was lower than that in women (14.38%). Meta-analysis results displayed pooled prevalence of osteoporosis of 18.0% (10.1%-25.8%) in total sample, 7.7% (5.7%-9.7%) in men and 22.4% (17.1%-27.6%) in women. Multivariable logistic regression models showed that ageing, women, low education level or income, drinking or underweight was related to increased risk for osteopenia or osteoporosis.

CONCLUSIONS

About one-sixth of the participants suffered osteoporosis in rural China, and the prevalence in women was higher than men. Although the results were lower than that of meta-analysis, osteoporosis still accounts for huge burden of disease in rural population due to limited medical service and lack of health risk awareness rather than urban area.

TRIAL REGISTRATION NUMBER

Chinese Clinical Trial Registry (ChiCTR-OOC-15006699; Pre-results).